Carsten Peterson
Expert
Self-organizing networks for extracting jet features
Författare
Summary, in English
Self-organizing neural networks are briefly reviewed and compared with supervised learning algorithms like back-propagation. The power of self-organization networks is in their capability of displaying typical features in a transparent manner. This is successfully demonstrated with two applications from hadronic jet physics; hadronization model discrimination and separation of b, c and light quarks.
Avdelning/ar
- Institutionen för astronomi och teoretisk fysik - Har omorganiserats
Publiceringsår
1991-12
Språk
Engelska
Sidor
193-209
Publikation/Tidskrift/Serie
Computer Physics Communications
Volym
67
Issue
2
Dokumenttyp
Artikel i tidskrift
Förlag
Elsevier
Ämne
- Subatomic Physics
Status
Published
ISBN/ISSN/Övrigt
- ISSN: 0010-4655